{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\research\works\주제14_내로남불_교육\politics and policy\data\code.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}20 Mar 2025, 18:00:23

{com}. meoprobit family_edu class education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    12,877
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        16
{col 63}{txt}avg{col 67}={res}{col 69}     378.7
{col 63}{txt}max{col 67}={res}{col 69}     1,005

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}    88.48
{txt}Log likelihood = {res}-17428.301{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2}  .000863{col 26}{space 2} .0061276{col 37}{space 1}    0.14{col 46}{space 3}0.888{col 54}{space 4}-.0111469{col 67}{space 3} .0128728
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0508527{col 26}{space 2} .0076748{col 37}{space 1}    6.63{col 46}{space 3}0.000{col 54}{space 4} .0358103{col 67}{space 3} .0658951
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0213999{col 26}{space 2} .0063279{col 37}{space 1}    3.38{col 46}{space 3}0.001{col 54}{space 4} .0089975{col 67}{space 3} .0338023
{txt}{space 6}gender {c |}{col 14}{res}{space 2} -.054687{col 26}{space 2} .0188873{col 37}{space 1}   -2.90{col 46}{space 3}0.004{col 54}{space 4}-.0917054{col 67}{space 3}-.0176686
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0039194{col 26}{space 2} .0194362{col 37}{space 1}   -0.20{col 46}{space 3}0.840{col 54}{space 4}-.0420136{col 67}{space 3} .0341748
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0280801{col 26}{space 2} .0215642{col 37}{space 1}    1.30{col 46}{space 3}0.193{col 54}{space 4}-.0141848{col 67}{space 3} .0703451
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0366371{col 26}{space 2} .0080881{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .0207848{col 67}{space 3} .0524894
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0368865{col 26}{space 2} .0286709{col 37}{space 1}   -1.29{col 46}{space 3}0.198{col 54}{space 4}-.0930805{col 67}{space 3} .0193076
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.380882{col 26}{space 2} .0817122{col 37}{space 1}  -16.90{col 46}{space 3}0.000{col 54}{space 4}-1.541035{col 67}{space 3} -1.22073
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.4337902{col 26}{space 2} .0807942{col 37}{space 1}   -5.37{col 46}{space 3}0.000{col 54}{space 4} -.592144{col 67}{space 3}-.2754364
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}  .654124{col 26}{space 2} .0809361{col 37}{space 1}    8.08{col 46}{space 3}0.000{col 54}{space 4} .4954922{col 67}{space 3} .8127558
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.811935{col 26}{space 2} .0818597{col 37}{space 1}   22.13{col 46}{space 3}0.000{col 54}{space 4} 1.651493{col 67}{space 3} 1.972377
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1161291{col 26}{space 2} .0303304{col 54}{space 4} .0696027{col 67}{space 3} .1937566
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1002.21{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,544
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     339.5
{col 63}{txt}max{col 67}={res}{col 69}       941

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   100.33
{txt}Log likelihood = {res}-15512.307{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0006302{col 26}{space 2} .0066202{col 37}{space 1}    0.10{col 46}{space 3}0.924{col 54}{space 4}-.0123451{col 67}{space 3} .0136055
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0001958{col 26}{space 2} .0100736{col 37}{space 1}    0.02{col 46}{space 3}0.984{col 54}{space 4}-.0195481{col 67}{space 3} .0199396
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0579757{col 26}{space 2} .0084232{col 37}{space 1}    6.88{col 46}{space 3}0.000{col 54}{space 4} .0414664{col 67}{space 3} .0744849
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0216655{col 26}{space 2} .0066947{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .0085441{col 67}{space 3} .0347869
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0595315{col 26}{space 2}  .020444{col 37}{space 1}   -2.91{col 46}{space 3}0.004{col 54}{space 4} -.099601{col 67}{space 3}-.0194621
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0003334{col 26}{space 2} .0206323{col 37}{space 1}    0.02{col 46}{space 3}0.987{col 54}{space 4}-.0401052{col 67}{space 3} .0407721
{txt}{space 4}religion {c |}{col 14}{res}{space 2}  .022035{col 26}{space 2} .0227376{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.0225299{col 67}{space 3} .0665998
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0418936{col 26}{space 2} .0085818{col 37}{space 1}    4.88{col 46}{space 3}0.000{col 54}{space 4} .0250736{col 67}{space 3} .0587136
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0403285{col 26}{space 2} .0299321{col 37}{space 1}   -1.35{col 46}{space 3}0.178{col 54}{space 4}-.0989944{col 67}{space 3} .0183373
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.372349{col 26}{space 2} .0864985{col 37}{space 1}  -15.87{col 46}{space 3}0.000{col 54}{space 4}-1.541883{col 67}{space 3}-1.202816
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.4154773{col 26}{space 2} .0855248{col 37}{space 1}   -4.86{col 46}{space 3}0.000{col 54}{space 4}-.5831027{col 67}{space 3}-.2478518
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .6912009{col 26}{space 2} .0857037{col 37}{space 1}    8.07{col 46}{space 3}0.000{col 54}{space 4} .5232248{col 67}{space 3}  .859177
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.863347{col 26}{space 2} .0867248{col 37}{space 1}   21.49{col 46}{space 3}0.000{col 54}{space 4}  1.69337{col 67}{space 3} 2.033325
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1235825{col 26}{space 2}  .032272{col 54}{space 4} .0740758{col 67}{space 3} .2061756
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 950.80{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,544
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     339.5
{col 63}{txt}max{col 67}={res}{col 69}       941

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   111.83
{txt}Log likelihood = {res}-15506.544{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2}-.0010759{col 26}{space 2} .0066536{col 37}{space 1}   -0.16{col 46}{space 3}0.872{col 54}{space 4}-.0141168{col 67}{space 3} .0119649
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0064241{col 26}{space 2} .0102949{col 37}{space 1}   -0.62{col 46}{space 3}0.533{col 54}{space 4}-.0266016{col 67}{space 3} .0137535
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0707316{col 26}{space 2}  .063819{col 37}{space 1}   -1.11{col 46}{space 3}0.268{col 54}{space 4}-.1958145{col 67}{space 3} .0543513
{txt}high_skilled {c |}{col 14}{res}{space 2} .0782516{col 26}{space 2} .0241314{col 37}{space 1}    3.24{col 46}{space 3}0.001{col 54}{space 4} .0309549{col 67}{space 3} .1255482
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0463358{col 26}{space 2} .0091622{col 37}{space 1}    5.06{col 46}{space 3}0.000{col 54}{space 4} .0283782{col 67}{space 3} .0642933
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0195274{col 26}{space 2} .0067331{col 37}{space 1}    2.90{col 46}{space 3}0.004{col 54}{space 4} .0063309{col 67}{space 3}  .032724
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0655545{col 26}{space 2} .0205235{col 37}{space 1}   -3.19{col 46}{space 3}0.001{col 54}{space 4}-.1057799{col 67}{space 3}-.0253292
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0006628{col 26}{space 2} .0206366{col 37}{space 1}   -0.03{col 46}{space 3}0.974{col 54}{space 4}-.0411097{col 67}{space 3} .0397842
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0234258{col 26}{space 2} .0227463{col 37}{space 1}    1.03{col 46}{space 3}0.303{col 54}{space 4}-.0211562{col 67}{space 3} .0680077
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0408876{col 26}{space 2} .0085876{col 37}{space 1}    4.76{col 46}{space 3}0.000{col 54}{space 4} .0240562{col 67}{space 3} .0577191
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0367842{col 26}{space 2} .0299997{col 37}{space 1}   -1.23{col 46}{space 3}0.220{col 54}{space 4}-.0955825{col 67}{space 3} .0220142
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -1.42179{col 26}{space 2} .0879632{col 37}{space 1}  -16.16{col 46}{space 3}0.000{col 54}{space 4}-1.594195{col 67}{space 3}-1.249386
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.4639365{col 26}{space 2} .0869532{col 37}{space 1}   -5.34{col 46}{space 3}0.000{col 54}{space 4}-.6343617{col 67}{space 3}-.2935113
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .6434579{col 26}{space 2} .0870931{col 37}{space 1}    7.39{col 46}{space 3}0.000{col 54}{space 4} .4727586{col 67}{space 3} .8141572
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.815843{col 26}{space 2} .0880846{col 37}{space 1}   20.61{col 46}{space 3}0.000{col 54}{space 4} 1.643201{col 67}{space 3} 1.988486
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1245247{col 26}{space 2}  .032516{col 54}{space 4}  .074643{col 67}{space 3}  .207741
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 949.38{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_1

. meoprobit family_edu class education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,514
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     338.6
{col 63}{txt}max{col 67}={res}{col 69}     1,202

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   137.99
{txt}Log likelihood = {res}-15512.555{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0180657{col 26}{space 2} .0067887{col 37}{space 1}    2.66{col 46}{space 3}0.008{col 54}{space 4} .0047601{col 67}{space 3} .0313713
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0511843{col 26}{space 2} .0083349{col 37}{space 1}    6.14{col 46}{space 3}0.000{col 54}{space 4} .0348482{col 67}{space 3} .0675204
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0463604{col 26}{space 2}  .006731{col 37}{space 1}    6.89{col 46}{space 3}0.000{col 54}{space 4} .0331678{col 67}{space 3} .0595529
{txt}{space 6}gender {c |}{col 14}{res}{space 2} -.012471{col 26}{space 2} .0198935{col 37}{space 1}   -0.63{col 46}{space 3}0.531{col 54}{space 4}-.0514615{col 67}{space 3} .0265195
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0095739{col 26}{space 2} .0212679{col 37}{space 1}   -0.45{col 46}{space 3}0.653{col 54}{space 4}-.0512582{col 67}{space 3} .0321104
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0423295{col 26}{space 2}  .025963{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.0932159{col 67}{space 3}  .008557
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0476776{col 26}{space 2} .0087143{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} .0305978{col 67}{space 3} .0647574
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0704707{col 26}{space 2}  .028104{col 37}{space 1}   -2.51{col 46}{space 3}0.012{col 54}{space 4}-.1255535{col 67}{space 3}-.0153879
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.207934{col 26}{space 2} .0874474{col 37}{space 1}  -13.81{col 46}{space 3}0.000{col 54}{space 4}-1.379328{col 67}{space 3} -1.03654
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.2143281{col 26}{space 2} .0868036{col 37}{space 1}   -2.47{col 46}{space 3}0.014{col 54}{space 4}  -.38446{col 67}{space 3}-.0441961
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .8996585{col 26}{space 2}   .08708{col 37}{space 1}   10.33{col 46}{space 3}0.000{col 54}{space 4} .7289849{col 67}{space 3} 1.070332
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.044708{col 26}{space 2} .0883389{col 37}{space 1}   23.15{col 46}{space 3}0.000{col 54}{space 4} 1.871567{col 67}{space 3} 2.217849
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1376951{col 26}{space 2} .0358625{col 54}{space 4} .0826464{col 67}{space 3} .2294102
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 964.88{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,241
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     301.2
{col 63}{txt}max{col 67}={res}{col 69}     1,062

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   131.90
{txt}Log likelihood = {res}-13710.422{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0176174{col 26}{space 2} .0073298{col 37}{space 1}    2.40{col 46}{space 3}0.016{col 54}{space 4} .0032513{col 67}{space 3} .0319834
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0012506{col 26}{space 2} .0104401{col 37}{space 1}   -0.12{col 46}{space 3}0.905{col 54}{space 4}-.0217128{col 67}{space 3} .0192117
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0556649{col 26}{space 2} .0091279{col 37}{space 1}    6.10{col 46}{space 3}0.000{col 54}{space 4} .0377745{col 67}{space 3} .0735552
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0439385{col 26}{space 2} .0071406{col 37}{space 1}    6.15{col 46}{space 3}0.000{col 54}{space 4} .0299431{col 67}{space 3} .0579338
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0183628{col 26}{space 2} .0219563{col 37}{space 1}   -0.84{col 46}{space 3}0.403{col 54}{space 4}-.0613963{col 67}{space 3} .0246707
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0207117{col 26}{space 2} .0227572{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4} -.065315{col 67}{space 3} .0238916
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0359702{col 26}{space 2} .0274016{col 37}{space 1}   -1.31{col 46}{space 3}0.189{col 54}{space 4}-.0896764{col 67}{space 3} .0177359
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0523752{col 26}{space 2} .0092529{col 37}{space 1}    5.66{col 46}{space 3}0.000{col 54}{space 4} .0342399{col 67}{space 3} .0705105
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0656771{col 26}{space 2} .0296097{col 37}{space 1}   -2.22{col 46}{space 3}0.027{col 54}{space 4}-.1237111{col 67}{space 3} -.007643
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.209655{col 26}{space 2} .0913381{col 37}{space 1}  -13.24{col 46}{space 3}0.000{col 54}{space 4}-1.388674{col 67}{space 3}-1.030635
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.2094635{col 26}{space 2} .0906623{col 37}{space 1}   -2.31{col 46}{space 3}0.021{col 54}{space 4}-.3871583{col 67}{space 3}-.0317687
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .9238769{col 26}{space 2} .0909711{col 37}{space 1}   10.16{col 46}{space 3}0.000{col 54}{space 4} .7455768{col 67}{space 3} 1.102177
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.078549{col 26}{space 2} .0923757{col 37}{space 1}   22.50{col 46}{space 3}0.000{col 54}{space 4} 1.897496{col 67}{space 3} 2.259603
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1391732{col 26}{space 2} .0365536{col 54}{space 4} .0831743{col 67}{space 3} .2328745
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 885.29{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit family_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,241
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     301.2
{col 63}{txt}max{col 67}={res}{col 69}     1,062

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   133.51
{txt}Log likelihood = {res}-13709.615{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  family_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0171867{col 26}{space 2}  .007385{col 37}{space 1}    2.33{col 46}{space 3}0.020{col 54}{space 4} .0027122{col 67}{space 3} .0316611
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0031144{col 26}{space 2} .0107346{col 37}{space 1}   -0.29{col 46}{space 3}0.772{col 54}{space 4}-.0241538{col 67}{space 3}  .017925
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0403028{col 26}{space 2} .0431929{col 37}{space 1}   -0.93{col 46}{space 3}0.351{col 54}{space 4}-.1249593{col 67}{space 3} .0443536
{txt}high_skilled {c |}{col 14}{res}{space 2} .0227027{col 26}{space 2} .0252877{col 37}{space 1}    0.90{col 46}{space 3}0.369{col 54}{space 4}-.0268604{col 67}{space 3} .0722657
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0529143{col 26}{space 2} .0096613{col 37}{space 1}    5.48{col 46}{space 3}0.000{col 54}{space 4} .0339785{col 67}{space 3} .0718502
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0437815{col 26}{space 2} .0071856{col 37}{space 1}    6.09{col 46}{space 3}0.000{col 54}{space 4} .0296979{col 67}{space 3}  .057865
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0209963{col 26}{space 2} .0220587{col 37}{space 1}   -0.95{col 46}{space 3}0.341{col 54}{space 4}-.0642306{col 67}{space 3} .0222379
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0204422{col 26}{space 2} .0227661{col 37}{space 1}   -0.90{col 46}{space 3}0.369{col 54}{space 4}-.0650629{col 67}{space 3} .0241786
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0356672{col 26}{space 2}  .027403{col 37}{space 1}   -1.30{col 46}{space 3}0.193{col 54}{space 4}-.0893761{col 67}{space 3} .0180418
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0517089{col 26}{space 2} .0092679{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} .0335441{col 67}{space 3} .0698737
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0670549{col 26}{space 2} .0296798{col 37}{space 1}   -2.26{col 46}{space 3}0.024{col 54}{space 4}-.1252262{col 67}{space 3}-.0088837
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.223121{col 26}{space 2} .0927099{col 37}{space 1}  -13.19{col 46}{space 3}0.000{col 54}{space 4}-1.404829{col 67}{space 3}-1.041412
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-.2225923{col 26}{space 2} .0920144{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.4029373{col 67}{space 3}-.0422474
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .9108169{col 26}{space 2} .0923127{col 37}{space 1}    9.87{col 46}{space 3}0.000{col 54}{space 4} .7298873{col 67}{space 3} 1.091746
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.065374{col 26}{space 2} .0936992{col 37}{space 1}   22.04{col 46}{space 3}0.000{col 54}{space 4} 1.881728{col 67}{space 3} 2.249021
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1395636{col 26}{space 2} .0366562{col 54}{space 4} .0834076{col 67}{space 3} .2335281
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 884.48{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_2

. meoprobit my_edu class education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    12,946
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        16
{col 63}{txt}avg{col 67}={res}{col 69}     380.8
{col 63}{txt}max{col 67}={res}{col 69}     1,014

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   185.41
{txt}Log likelihood = {res}-15168.431{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0116668{col 26}{space 2} .0062831{col 37}{space 1}    1.86{col 46}{space 3}0.063{col 54}{space 4}-.0006479{col 67}{space 3} .0239815
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0864888{col 26}{space 2} .0078935{col 37}{space 1}   10.96{col 46}{space 3}0.000{col 54}{space 4} .0710178{col 67}{space 3} .1019598
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0091949{col 26}{space 2} .0065055{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.0035556{col 67}{space 3} .0219455
{txt}{space 6}gender {c |}{col 14}{res}{space 2}  .053027{col 26}{space 2} .0194009{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .0150019{col 67}{space 3} .0910521
{txt}{space 5}marital {c |}{col 14}{res}{space 2} -.006097{col 26}{space 2} .0199609{col 37}{space 1}   -0.31{col 46}{space 3}0.760{col 54}{space 4}-.0452196{col 67}{space 3} .0330257
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0577125{col 26}{space 2} .0221412{col 37}{space 1}    2.61{col 46}{space 3}0.009{col 54}{space 4} .0143165{col 67}{space 3} .1011084
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0056682{col 26}{space 2} .0082956{col 37}{space 1}    0.68{col 46}{space 3}0.494{col 54}{space 4}-.0105909{col 67}{space 3} .0219274
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2253426{col 26}{space 2} .0293399{col 37}{space 1}   -7.68{col 46}{space 3}0.000{col 54}{space 4}-.2828478{col 67}{space 3}-.1678374
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.073608{col 26}{space 2}  .086556{col 37}{space 1}  -23.96{col 46}{space 3}0.000{col 54}{space 4}-2.243254{col 67}{space 3}-1.903961
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.317857{col 26}{space 2} .0822888{col 37}{space 1}  -16.02{col 46}{space 3}0.000{col 54}{space 4} -1.47914{col 67}{space 3}-1.156574
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.2075206{col 26}{space 2} .0813475{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}-.3669588{col 67}{space 3}-.0480824
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.162483{col 26}{space 2} .0817265{col 37}{space 1}   14.22{col 46}{space 3}0.000{col 54}{space 4} 1.002302{col 67}{space 3} 1.322664
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1140384{col 26}{space 2} .0293536{col 54}{space 4} .0688574{col 67}{space 3}  .188865
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1107.61{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,605
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     341.3
{col 63}{txt}max{col 67}={res}{col 69}       948

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   183.11
{txt}Log likelihood = {res}-13509.277{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0105708{col 26}{space 2} .0067856{col 37}{space 1}    1.56{col 46}{space 3}0.119{col 54}{space 4}-.0027288{col 67}{space 3} .0238703
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0120181{col 26}{space 2} .0103597{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.0082866{col 67}{space 3} .0323227
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .088611{col 26}{space 2} .0086542{col 37}{space 1}   10.24{col 46}{space 3}0.000{col 54}{space 4} .0716492{col 67}{space 3} .1055729
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0105183{col 26}{space 2} .0068824{col 37}{space 1}    1.53{col 46}{space 3}0.126{col 54}{space 4} -.002971{col 67}{space 3} .0240075
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0527787{col 26}{space 2} .0210034{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .0116129{col 67}{space 3} .0939446
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0084019{col 26}{space 2} .0211909{col 37}{space 1}   -0.40{col 46}{space 3}0.692{col 54}{space 4}-.0499352{col 67}{space 3} .0331314
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0519691{col 26}{space 2} .0233491{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0062057{col 67}{space 3} .0977325
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0114149{col 26}{space 2} .0088005{col 37}{space 1}    1.30{col 46}{space 3}0.195{col 54}{space 4}-.0058337{col 67}{space 3} .0286635
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2270885{col 26}{space 2} .0306396{col 37}{space 1}   -7.41{col 46}{space 3}0.000{col 54}{space 4}-.2871411{col 67}{space 3}-.1670359
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.064745{col 26}{space 2} .0917349{col 37}{space 1}  -22.51{col 46}{space 3}0.000{col 54}{space 4}-2.244542{col 67}{space 3}-1.884948
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.281432{col 26}{space 2} .0867686{col 37}{space 1}  -14.77{col 46}{space 3}0.000{col 54}{space 4}-1.451495{col 67}{space 3}-1.111368
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.1514452{col 26}{space 2} .0858069{col 37}{space 1}   -1.76{col 46}{space 3}0.078{col 54}{space 4}-.3196237{col 67}{space 3} .0167332
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.229705{col 26}{space 2} .0862529{col 37}{space 1}   14.26{col 46}{space 3}0.000{col 54}{space 4} 1.060652{col 67}{space 3} 1.398758
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1190548{col 26}{space 2} .0308469{col 54}{space 4} .0716477{col 67}{space 3}   .19783
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1012.21{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,605
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     341.3
{col 63}{txt}max{col 67}={res}{col 69}       948

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   189.50
{txt}Log likelihood = {res}-13506.053{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0090983{col 26}{space 2} .0068191{col 37}{space 1}    1.33{col 46}{space 3}0.182{col 54}{space 4}-.0042669{col 67}{space 3} .0224635
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0066642{col 26}{space 2} .0105831{col 37}{space 1}    0.63{col 46}{space 3}0.529{col 54}{space 4}-.0140784{col 67}{space 3} .0274067
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0239958{col 26}{space 2} .0660819{col 37}{space 1}   -0.36{col 46}{space 3}0.717{col 54}{space 4} -.153514{col 67}{space 3} .1055224
{txt}high_skilled {c |}{col 14}{res}{space 2} .0625352{col 26}{space 2} .0247844{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4} .0139586{col 67}{space 3} .1111117
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .079327{col 26}{space 2}  .009406{col 37}{space 1}    8.43{col 46}{space 3}0.000{col 54}{space 4} .0608915{col 67}{space 3} .0977624
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0087872{col 26}{space 2}   .00692{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0047757{col 67}{space 3} .0223501
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0485045{col 26}{space 2} .0210801{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0071882{col 67}{space 3} .0898207
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0092592{col 26}{space 2} .0211951{col 37}{space 1}   -0.44{col 46}{space 3}0.662{col 54}{space 4}-.0508009{col 67}{space 3} .0322824
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0531815{col 26}{space 2} .0233577{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4} .0074011{col 67}{space 3} .0989618
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0106363{col 26}{space 2} .0088071{col 37}{space 1}    1.21{col 46}{space 3}0.227{col 54}{space 4}-.0066252{col 67}{space 3} .0278979
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2236738{col 26}{space 2} .0307067{col 37}{space 1}   -7.28{col 46}{space 3}0.000{col 54}{space 4}-.2838579{col 67}{space 3}-.1634896
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.103301{col 26}{space 2} .0930514{col 37}{space 1}  -22.60{col 46}{space 3}0.000{col 54}{space 4}-2.285678{col 67}{space 3}-1.920923
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.319762{col 26}{space 2} .0881439{col 37}{space 1}  -14.97{col 46}{space 3}0.000{col 54}{space 4}-1.492521{col 67}{space 3}-1.147003
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.1891635{col 26}{space 2} .0871545{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4}-.3599832{col 67}{space 3}-.0183437
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.192377{col 26}{space 2} .0875625{col 37}{space 1}   13.62{col 46}{space 3}0.000{col 54}{space 4} 1.020758{col 67}{space 3} 1.363996
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1194079{col 26}{space 2} .0309351{col 54}{space 4} .0718639{col 67}{space 3}  .198406
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1010.49{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_3

. meoprobit my_edu class education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,582
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     340.6
{col 63}{txt}max{col 67}={res}{col 69}     1,204

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   312.46
{txt}Log likelihood = {res}-13524.133{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0354691{col 26}{space 2} .0069497{col 37}{space 1}    5.10{col 46}{space 3}0.000{col 54}{space 4} .0218479{col 67}{space 3} .0490902
{txt}{space 3}education {c |}{col 14}{res}{space 2}   .11144{col 26}{space 2} .0085891{col 37}{space 1}   12.97{col 46}{space 3}0.000{col 54}{space 4} .0946056{col 67}{space 3} .1282744
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0282588{col 26}{space 2} .0068966{col 37}{space 1}    4.10{col 46}{space 3}0.000{col 54}{space 4} .0147417{col 67}{space 3} .0417759
{txt}{space 6}gender {c |}{col 14}{res}{space 2}   .02262{col 26}{space 2} .0203949{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0173533{col 67}{space 3} .0625933
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0322799{col 26}{space 2} .0217983{col 37}{space 1}   -1.48{col 46}{space 3}0.139{col 54}{space 4}-.0750037{col 67}{space 3} .0104439
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0010707{col 26}{space 2}  .026565{col 37}{space 1}   -0.04{col 46}{space 3}0.968{col 54}{space 4}-.0531371{col 67}{space 3} .0509957
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0286093{col 26}{space 2} .0089311{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .0111046{col 67}{space 3}  .046114
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2531403{col 26}{space 2} .0287527{col 37}{space 1}   -8.80{col 46}{space 3}0.000{col 54}{space 4}-.3094946{col 67}{space 3} -.196786
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.896859{col 26}{space 2} .0903398{col 37}{space 1}  -21.00{col 46}{space 3}0.000{col 54}{space 4}-2.073922{col 67}{space 3}-1.719796
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.117581{col 26}{space 2} .0857287{col 37}{space 1}  -13.04{col 46}{space 3}0.000{col 54}{space 4}-1.285606{col 67}{space 3}-.9495562
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}  .084412{col 26}{space 2} .0849568{col 37}{space 1}    0.99{col 46}{space 3}0.320{col 54}{space 4}-.0821003{col 67}{space 3} .2509244
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.464937{col 26}{space 2} .0856298{col 37}{space 1}   17.11{col 46}{space 3}0.000{col 54}{space 4} 1.297106{col 67}{space 3} 1.632768
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1217915{col 26}{space 2} .0313146{col 54}{space 4} .0735799{col 67}{space 3} .2015926
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1045.13{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,301
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     303.0
{col 63}{txt}max{col 67}={res}{col 69}     1,064

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   317.35
{txt}Log likelihood = {res}-11957.587{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2}  .037892{col 26}{space 2} .0075062{col 37}{space 1}    5.05{col 46}{space 3}0.000{col 54}{space 4} .0231801{col 67}{space 3} .0526039
{txt}{space 6}income {c |}{col 14}{res}{space 2}  .002195{col 26}{space 2} .0107126{col 37}{space 1}    0.20{col 46}{space 3}0.838{col 54}{space 4}-.0188013{col 67}{space 3} .0231914
{txt}{space 3}education {c |}{col 14}{res}{space 2} .1233371{col 26}{space 2} .0094163{col 37}{space 1}   13.10{col 46}{space 3}0.000{col 54}{space 4} .1048815{col 67}{space 3} .1417926
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0279556{col 26}{space 2} .0073201{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .0136084{col 67}{space 3} .0423027
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0113497{col 26}{space 2} .0225249{col 37}{space 1}    0.50{col 46}{space 3}0.614{col 54}{space 4}-.0327983{col 67}{space 3} .0554976
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0332351{col 26}{space 2} .0233281{col 37}{space 1}   -1.42{col 46}{space 3}0.154{col 54}{space 4}-.0789573{col 67}{space 3} .0124871
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0053143{col 26}{space 2} .0280479{col 37}{space 1}    0.19{col 46}{space 3}0.850{col 54}{space 4}-.0496587{col 67}{space 3} .0602873
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0273199{col 26}{space 2} .0094838{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4}  .008732{col 67}{space 3} .0459078
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2404971{col 26}{space 2} .0302944{col 37}{space 1}   -7.94{col 46}{space 3}0.000{col 54}{space 4}-.2998731{col 67}{space 3}-.1811211
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.859844{col 26}{space 2} .0953256{col 37}{space 1}  -19.51{col 46}{space 3}0.000{col 54}{space 4}-2.046679{col 67}{space 3}-1.673009
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.084331{col 26}{space 2} .0902608{col 37}{space 1}  -12.01{col 46}{space 3}0.000{col 54}{space 4}-1.261239{col 67}{space 3}-.9074235
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .1327437{col 26}{space 2} .0894745{col 37}{space 1}    1.48{col 46}{space 3}0.138{col 54}{space 4}-.0426231{col 67}{space 3} .3081106
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.523235{col 26}{space 2} .0902493{col 37}{space 1}   16.88{col 46}{space 3}0.000{col 54}{space 4} 1.346349{col 67}{space 3}  1.70012
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1255717{col 26}{space 2} .0325788{col 54}{space 4} .0755184{col 67}{space 3} .2088003
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 939.29{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,301
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     303.0
{col 63}{txt}max{col 67}={res}{col 69}     1,064

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   321.71
{txt}Log likelihood = {res}-11955.371{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0373358{col 26}{space 2} .0075626{col 37}{space 1}    4.94{col 46}{space 3}0.000{col 54}{space 4} .0225134{col 67}{space 3} .0521582
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0005308{col 26}{space 2} .0110086{col 37}{space 1}   -0.05{col 46}{space 3}0.962{col 54}{space 4}-.0221073{col 67}{space 3} .0210456
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0741839{col 26}{space 2} .0444091{col 37}{space 1}   -1.67{col 46}{space 3}0.095{col 54}{space 4}-.1612241{col 67}{space 3} .0128563
{txt}high_skilled {c |}{col 14}{res}{space 2} .0349683{col 26}{space 2}  .025942{col 37}{space 1}    1.35{col 46}{space 3}0.178{col 54}{space 4} -.015877{col 67}{space 3} .0858137
{txt}{space 3}education {c |}{col 14}{res}{space 2} .1191099{col 26}{space 2} .0099617{col 37}{space 1}   11.96{col 46}{space 3}0.000{col 54}{space 4} .0995854{col 67}{space 3} .1386344
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0278153{col 26}{space 2} .0073667{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .0133768{col 67}{space 3} .0422537
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0069231{col 26}{space 2} .0226256{col 37}{space 1}    0.31{col 46}{space 3}0.760{col 54}{space 4}-.0374222{col 67}{space 3} .0512684
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0326253{col 26}{space 2} .0233377{col 37}{space 1}   -1.40{col 46}{space 3}0.162{col 54}{space 4}-.0783663{col 67}{space 3} .0131157
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0058626{col 26}{space 2} .0280508{col 37}{space 1}    0.21{col 46}{space 3}0.834{col 54}{space 4} -.049116{col 67}{space 3} .0608412
{txt}{space 7}urban {c |}{col 14}{res}{space 2}   .02621{col 26}{space 2}  .009499{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .0075924{col 67}{space 3} .0448277
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} -.243052{col 26}{space 2}  .030364{col 37}{space 1}   -8.00{col 46}{space 3}0.000{col 54}{space 4}-.3025643{col 67}{space 3}-.1835398
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.880956{col 26}{space 2} .0967109{col 37}{space 1}  -19.45{col 46}{space 3}0.000{col 54}{space 4}-2.070506{col 67}{space 3}-1.691406
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} -1.10482{col 26}{space 2} .0917004{col 37}{space 1}  -12.05{col 46}{space 3}0.000{col 54}{space 4} -1.28455{col 67}{space 3}-.9250907
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .1128012{col 26}{space 2} .0908886{col 37}{space 1}    1.24{col 46}{space 3}0.215{col 54}{space 4}-.0653373{col 67}{space 3} .2909396
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.503546{col 26}{space 2} .0916376{col 37}{space 1}   16.41{col 46}{space 3}0.000{col 54}{space 4}  1.32394{col 67}{space 3} 1.683153
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1259368{col 26}{space 2} .0326782{col 54}{space 4} .0757324{col 67}{space 3} .2094226
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 936.78{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_4

. meoprobit edu_fair class education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    12,797
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        16
{col 63}{txt}avg{col 67}={res}{col 69}     376.4
{col 63}{txt}max{col 67}={res}{col 69}     1,010

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   234.56
{txt}Log likelihood = {res}-16799.099{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0532393{col 26}{space 2} .0064486{col 37}{space 1}    8.26{col 46}{space 3}0.000{col 54}{space 4} .0406003{col 67}{space 3} .0658783
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0499774{col 26}{space 2} .0080008{col 37}{space 1}   -6.25{col 46}{space 3}0.000{col 54}{space 4}-.0656588{col 67}{space 3}-.0342961
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0138522{col 26}{space 2} .0066203{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-.0268278{col 67}{space 3}-.0008767
{txt}{space 6}gender {c |}{col 14}{res}{space 2} -.145678{col 26}{space 2} .0197588{col 37}{space 1}   -7.37{col 46}{space 3}0.000{col 54}{space 4}-.1844046{col 67}{space 3}-.1069515
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0243848{col 26}{space 2} .0203581{col 37}{space 1}    1.20{col 46}{space 3}0.231{col 54}{space 4}-.0155163{col 67}{space 3}  .064286
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1705682{col 26}{space 2} .0226862{col 37}{space 1}    7.52{col 46}{space 3}0.000{col 54}{space 4}  .126104{col 67}{space 3} .2150323
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0487858{col 26}{space 2} .0085084{col 37}{space 1}    5.73{col 46}{space 3}0.000{col 54}{space 4} .0321097{col 67}{space 3}  .065462
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}  .010797{col 26}{space 2}  .030101{col 37}{space 1}    0.36{col 46}{space 3}0.720{col 54}{space 4}-.0481999{col 67}{space 3} .0697938
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0676245{col 26}{space 2} .0932442{col 37}{space 1}   -0.73{col 46}{space 3}0.468{col 54}{space 4}-.2503798{col 67}{space 3} .1151308
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .7729004{col 26}{space 2} .0934347{col 37}{space 1}    8.27{col 46}{space 3}0.000{col 54}{space 4} .5897717{col 67}{space 3}  .956029
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.342321{col 26}{space 2} .0937394{col 37}{space 1}   14.32{col 46}{space 3}0.000{col 54}{space 4} 1.158595{col 67}{space 3} 1.526046
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}  2.03659{col 26}{space 2} .0948554{col 37}{space 1}   21.47{col 46}{space 3}0.000{col 54}{space 4} 1.850676{col 67}{space 3} 2.222503
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1798874{col 26}{space 2} .0459363{col 54}{space 4} .1090528{col 67}{space 3} .2967321
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1249.97{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,483
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     337.7
{col 63}{txt}max{col 67}={res}{col 69}       945

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   230.94
{txt}Log likelihood = {res}-14957.244{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0501592{col 26}{space 2} .0069625{col 37}{space 1}    7.20{col 46}{space 3}0.000{col 54}{space 4}  .036513{col 67}{space 3} .0638055
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0291516{col 26}{space 2} .0105464{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .0084809{col 67}{space 3} .0498223
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0580928{col 26}{space 2} .0087811{col 37}{space 1}   -6.62{col 46}{space 3}0.000{col 54}{space 4}-.0753034{col 67}{space 3}-.0408822
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0121749{col 26}{space 2} .0070069{col 37}{space 1}   -1.74{col 46}{space 3}0.082{col 54}{space 4}-.0259082{col 67}{space 3} .0015583
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1444432{col 26}{space 2} .0214372{col 37}{space 1}   -6.74{col 46}{space 3}0.000{col 54}{space 4}-.1864594{col 67}{space 3} -.102427
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0103753{col 26}{space 2} .0216444{col 37}{space 1}    0.48{col 46}{space 3}0.632{col 54}{space 4}-.0320469{col 67}{space 3} .0527975
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1793576{col 26}{space 2} .0239501{col 37}{space 1}    7.49{col 46}{space 3}0.000{col 54}{space 4} .1324163{col 67}{space 3} .2262988
{txt}{space 7}urban {c |}{col 14}{res}{space 2}  .048012{col 26}{space 2} .0090344{col 37}{space 1}    5.31{col 46}{space 3}0.000{col 54}{space 4} .0303049{col 67}{space 3} .0657192
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} .0305173{col 26}{space 2} .0313855{col 37}{space 1}    0.97{col 46}{space 3}0.331{col 54}{space 4}-.0309971{col 67}{space 3} .0920318
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0218348{col 26}{space 2} .0950879{col 37}{space 1}   -0.23{col 46}{space 3}0.818{col 54}{space 4}-.2082037{col 67}{space 3} .1645341
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .8237371{col 26}{space 2} .0953297{col 37}{space 1}    8.64{col 46}{space 3}0.000{col 54}{space 4} .6368943{col 67}{space 3}  1.01058
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.389508{col 26}{space 2} .0956749{col 37}{space 1}   14.52{col 46}{space 3}0.000{col 54}{space 4} 1.201989{col 67}{space 3} 1.577028
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.082226{col 26}{space 2} .0969175{col 37}{space 1}   21.48{col 46}{space 3}0.000{col 54}{space 4} 1.892272{col 67}{space 3} 2.272181
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1701605{col 26}{space 2} .0435508{col 54}{space 4} .1030392{col 67}{space 3} .2810056
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1061.99{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,483
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     337.7
{col 63}{txt}max{col 67}={res}{col 69}       945

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   233.62
{txt}Log likelihood = {res}-14955.895{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0490861{col 26}{space 2} .0069966{col 37}{space 1}    7.02{col 46}{space 3}0.000{col 54}{space 4} .0353729{col 67}{space 3} .0627993
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0262589{col 26}{space 2} .0107787{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4}  .005133{col 67}{space 3} .0473847
{txt}{space 4}employer {c |}{col 14}{res}{space 2} .0733103{col 26}{space 2} .0661101{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0562631{col 67}{space 3} .2028837
{txt}high_skilled {c |}{col 14}{res}{space 2} .0296932{col 26}{space 2} .0252325{col 37}{space 1}    1.18{col 46}{space 3}0.239{col 54}{space 4}-.0197615{col 67}{space 3}  .079148
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0624846{col 26}{space 2} .0095337{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0811704{col 67}{space 3}-.0437988
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0131649{col 26}{space 2} .0070433{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.0269696{col 67}{space 3} .0006398
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1455834{col 26}{space 2} .0215175{col 37}{space 1}   -6.77{col 46}{space 3}0.000{col 54}{space 4}-.1877569{col 67}{space 3}-.1034099
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0097315{col 26}{space 2} .0216492{col 37}{space 1}    0.45{col 46}{space 3}0.653{col 54}{space 4}-.0327002{col 67}{space 3} .0521631
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1804101{col 26}{space 2} .0239604{col 37}{space 1}    7.53{col 46}{space 3}0.000{col 54}{space 4} .1334487{col 67}{space 3} .2273716
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0478219{col 26}{space 2}  .009041{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .0301018{col 67}{space 3} .0655419
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} .0339726{col 26}{space 2} .0314566{col 37}{space 1}    1.08{col 46}{space 3}0.280{col 54}{space 4}-.0276812{col 67}{space 3} .0956264
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0390421{col 26}{space 2} .0963224{col 37}{space 1}   -0.41{col 46}{space 3}0.685{col 54}{space 4}-.2278305{col 67}{space 3} .1497464
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .8066592{col 26}{space 2} .0965502{col 37}{space 1}    8.35{col 46}{space 3}0.000{col 54}{space 4} .6174242{col 67}{space 3} .9958941
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.372634{col 26}{space 2} .0968785{col 37}{space 1}   14.17{col 46}{space 3}0.000{col 54}{space 4} 1.182756{col 67}{space 3} 1.562513
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.065551{col 26}{space 2} .0981012{col 37}{space 1}   21.06{col 46}{space 3}0.000{col 54}{space 4} 1.873276{col 67}{space 3} 2.257826
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1704329{col 26}{space 2} .0436244{col 54}{space 4} .1031996{col 67}{space 3} .2814681
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1056.52{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_5

. meoprobit edu_fair class education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,398
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     335.2
{col 63}{txt}max{col 67}={res}{col 69}     1,204

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   263.32
{txt}Log likelihood = {res}-16717.961{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0629312{col 26}{space 2}  .006945{col 37}{space 1}    9.06{col 46}{space 3}0.000{col 54}{space 4} .0493193{col 67}{space 3} .0765431
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0461434{col 26}{space 2} .0084331{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} .0296148{col 67}{space 3} .0626719
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0077897{col 26}{space 2} .0067913{col 37}{space 1}   -1.15{col 46}{space 3}0.251{col 54}{space 4}-.0211004{col 67}{space 3} .0055211
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1248034{col 26}{space 2} .0201564{col 37}{space 1}   -6.19{col 46}{space 3}0.000{col 54}{space 4}-.1643092{col 67}{space 3}-.0852977
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0011343{col 26}{space 2} .0215314{col 37}{space 1}   -0.05{col 46}{space 3}0.958{col 54}{space 4}-.0433352{col 67}{space 3} .0410665
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0065959{col 26}{space 2} .0261552{col 37}{space 1}   -0.25{col 46}{space 3}0.801{col 54}{space 4}-.0578592{col 67}{space 3} .0446674
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0441035{col 26}{space 2} .0088247{col 37}{space 1}    5.00{col 46}{space 3}0.000{col 54}{space 4} .0268074{col 67}{space 3} .0613997
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.1031112{col 26}{space 2} .0285976{col 37}{space 1}   -3.61{col 46}{space 3}0.000{col 54}{space 4}-.1591614{col 67}{space 3} -.047061
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.2100996{col 26}{space 2} .1000916{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.4062756{col 67}{space 3}-.0139236
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .5731446{col 26}{space 2} .1002551{col 37}{space 1}    5.72{col 46}{space 3}0.000{col 54}{space 4} .3766482{col 67}{space 3}  .769641
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.257659{col 26}{space 2} .1005506{col 37}{space 1}   12.51{col 46}{space 3}0.000{col 54}{space 4} 1.060584{col 67}{space 3} 1.454735
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.095138{col 26}{space 2} .1014029{col 37}{space 1}   20.66{col 46}{space 3}0.000{col 54}{space 4} 1.896392{col 67}{space 3} 2.293884
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2189861{col 26}{space 2} .0552707{col 54}{space 4} .1335299{col 67}{space 3} .3591324
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1284.00{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,153
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     298.6
{col 63}{txt}max{col 67}={res}{col 69}     1,063

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   273.52
{txt}Log likelihood = {res}-14881.003{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0573868{col 26}{space 2} .0074984{col 37}{space 1}    7.65{col 46}{space 3}0.000{col 54}{space 4} .0426902{col 67}{space 3} .0720834
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0472339{col 26}{space 2} .0105413{col 37}{space 1}    4.48{col 46}{space 3}0.000{col 54}{space 4} .0265734{col 67}{space 3} .0678944
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0372073{col 26}{space 2} .0092085{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4}  .019159{col 67}{space 3} .0552556
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0086371{col 26}{space 2} .0072021{col 37}{space 1}   -1.20{col 46}{space 3}0.230{col 54}{space 4} -.022753{col 67}{space 3} .0054789
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1155254{col 26}{space 2} .0222141{col 37}{space 1}   -5.20{col 46}{space 3}0.000{col 54}{space 4}-.1590642{col 67}{space 3}-.0719865
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0213708{col 26}{space 2} .0230022{col 37}{space 1}   -0.93{col 46}{space 3}0.353{col 54}{space 4}-.0664543{col 67}{space 3} .0237127
{txt}{space 4}religion {c |}{col 14}{res}{space 2} -.004656{col 26}{space 2} .0275842{col 37}{space 1}   -0.17{col 46}{space 3}0.866{col 54}{space 4}-.0587201{col 67}{space 3} .0494081
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0444567{col 26}{space 2}  .009353{col 37}{space 1}    4.75{col 46}{space 3}0.000{col 54}{space 4} .0261251{col 67}{space 3} .0627883
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0892611{col 26}{space 2} .0300847{col 37}{space 1}   -2.97{col 46}{space 3}0.003{col 54}{space 4}-.1482261{col 67}{space 3}-.0302962
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -.146254{col 26}{space 2} .1029086{col 37}{space 1}   -1.42{col 46}{space 3}0.155{col 54}{space 4}-.3479511{col 67}{space 3} .0554431
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .6363939{col 26}{space 2} .1031301{col 37}{space 1}    6.17{col 46}{space 3}0.000{col 54}{space 4} .4342626{col 67}{space 3} .8385252
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.324618{col 26}{space 2} .1034733{col 37}{space 1}   12.80{col 46}{space 3}0.000{col 54}{space 4} 1.121814{col 67}{space 3} 1.527422
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.159614{col 26}{space 2} .1044006{col 37}{space 1}   20.69{col 46}{space 3}0.000{col 54}{space 4} 1.954992{col 67}{space 3} 2.364235
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2164518{col 26}{space 2} .0548951{col 54}{space 4} .1316695{col 67}{space 3} .3558257
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1151.25{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit edu_fair class income employer high_skilled education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,153
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     298.6
{col 63}{txt}max{col 67}={res}{col 69}     1,063

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   277.26
{txt}Log likelihood = {res}-14879.125{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0563778{col 26}{space 2} .0075537{col 37}{space 1}    7.46{col 46}{space 3}0.000{col 54}{space 4} .0415729{col 67}{space 3} .0711827
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0453686{col 26}{space 2} .0108467{col 37}{space 1}    4.18{col 46}{space 3}0.000{col 54}{space 4} .0241095{col 67}{space 3} .0666276
{txt}{space 4}employer {c |}{col 14}{res}{space 2}  .081399{col 26}{space 2} .0434819{col 37}{space 1}    1.87{col 46}{space 3}0.061{col 54}{space 4} -.003824{col 67}{space 3} .1666221
{txt}high_skilled {c |}{col 14}{res}{space 2} .0111899{col 26}{space 2} .0255209{col 37}{space 1}    0.44{col 46}{space 3}0.661{col 54}{space 4}-.0388301{col 67}{space 3} .0612099
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0356169{col 26}{space 2} .0097477{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .0165117{col 67}{space 3}  .054722
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0098002{col 26}{space 2} .0072448{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-.0239998{col 67}{space 3} .0043994
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1126795{col 26}{space 2} .0223124{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4} -.156411{col 67}{space 3} -.068948
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0227148{col 26}{space 2} .0230133{col 37}{space 1}   -0.99{col 46}{space 3}0.324{col 54}{space 4}-.0678199{col 67}{space 3} .0223904
{txt}{space 4}religion {c |}{col 14}{res}{space 2} -.005062{col 26}{space 2}  .027586{col 37}{space 1}   -0.18{col 46}{space 3}0.854{col 54}{space 4}-.0591295{col 67}{space 3} .0490055
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0449806{col 26}{space 2} .0093695{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .0266167{col 67}{space 3} .0633445
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0852006{col 26}{space 2} .0301569{col 37}{space 1}   -2.83{col 46}{space 3}0.005{col 54}{space 4} -.144307{col 67}{space 3}-.0260943
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -.154554{col 26}{space 2} .1040248{col 37}{space 1}   -1.49{col 46}{space 3}0.137{col 54}{space 4} -.358439{col 67}{space 3} .0493309
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .6282093{col 26}{space 2} .1042421{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .4238984{col 67}{space 3} .8325201
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.316641{col 26}{space 2} .1045709{col 37}{space 1}   12.59{col 46}{space 3}0.000{col 54}{space 4} 1.111686{col 67}{space 3} 1.521596
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}  2.15189{col 26}{space 2} .1054817{col 37}{space 1}   20.40{col 46}{space 3}0.000{col 54}{space 4}  1.94515{col 67}{space 3}  2.35863
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2160075{col 26}{space 2} .0547783{col 54}{space 4} .1314041{col 67}{space 3} .3550821
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1146.39{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_6

. coefplot (edu_1) (edu_3) (edu_5)||(edu_2) (edu_4) (edu_6), eform drop(_cons) omitted yline(1, lcolor(black) lwidth(thin)) lpattern(dash) graphregion(fcolor(white)) mlabel format(%9.2f) mlabposition(8) mlabsize(tiny) level(95) keep (class) vertical
{res}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. meoprobit edu_fair class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,483
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     337.7
{col 63}{txt}max{col 67}={res}{col 69}       945

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   233.62
{txt}Log likelihood = {res}-14955.895{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0490861{col 26}{space 2} .0069966{col 37}{space 1}    7.02{col 46}{space 3}0.000{col 54}{space 4} .0353729{col 67}{space 3} .0627993
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0262589{col 26}{space 2} .0107787{col 37}{space 1}    2.44{col 46}{space 3}0.015{col 54}{space 4}  .005133{col 67}{space 3} .0473847
{txt}{space 4}employer {c |}{col 14}{res}{space 2} .0733103{col 26}{space 2} .0661101{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0562631{col 67}{space 3} .2028837
{txt}high_skilled {c |}{col 14}{res}{space 2} .0296932{col 26}{space 2} .0252325{col 37}{space 1}    1.18{col 46}{space 3}0.239{col 54}{space 4}-.0197615{col 67}{space 3}  .079148
{txt}{space 3}education {c |}{col 14}{res}{space 2}-.0624846{col 26}{space 2} .0095337{col 37}{space 1}   -6.55{col 46}{space 3}0.000{col 54}{space 4}-.0811704{col 67}{space 3}-.0437988
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0131649{col 26}{space 2} .0070433{col 37}{space 1}   -1.87{col 46}{space 3}0.062{col 54}{space 4}-.0269696{col 67}{space 3} .0006398
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1455834{col 26}{space 2} .0215175{col 37}{space 1}   -6.77{col 46}{space 3}0.000{col 54}{space 4}-.1877569{col 67}{space 3}-.1034099
{txt}{space 5}marital {c |}{col 14}{res}{space 2} .0097315{col 26}{space 2} .0216492{col 37}{space 1}    0.45{col 46}{space 3}0.653{col 54}{space 4}-.0327002{col 67}{space 3} .0521631
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .1804101{col 26}{space 2} .0239604{col 37}{space 1}    7.53{col 46}{space 3}0.000{col 54}{space 4} .1334487{col 67}{space 3} .2273716
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0478219{col 26}{space 2}  .009041{col 37}{space 1}    5.29{col 46}{space 3}0.000{col 54}{space 4} .0301018{col 67}{space 3} .0655419
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} .0339726{col 26}{space 2} .0314566{col 37}{space 1}    1.08{col 46}{space 3}0.280{col 54}{space 4}-.0276812{col 67}{space 3} .0956264
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-.0390421{col 26}{space 2} .0963224{col 37}{space 1}   -0.41{col 46}{space 3}0.685{col 54}{space 4}-.2278305{col 67}{space 3} .1497464
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .8066592{col 26}{space 2} .0965502{col 37}{space 1}    8.35{col 46}{space 3}0.000{col 54}{space 4} .6174242{col 67}{space 3} .9958941
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.372634{col 26}{space 2} .0968785{col 37}{space 1}   14.17{col 46}{space 3}0.000{col 54}{space 4} 1.182756{col 67}{space 3} 1.562513
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 2.065551{col 26}{space 2} .0981012{col 37}{space 1}   21.06{col 46}{space 3}0.000{col 54}{space 4} 1.873276{col 67}{space 3} 2.257826
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1704329{col 26}{space 2} .0436244{col 54}{space 4} .1031996{col 67}{space 3} .2814681
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1056.52{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. margins, at(class =(1(1)10)) atmeans noatlegend 
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}    11,483
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Marginal predicted mean (1.edu_fair), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Marginal predicted mean (2.edu_fair), predict(pr outcome(2))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Marginal predicted mean (3.edu_fair), predict(pr outcome(3))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._predict}:{space 1}{res:Marginal predicted mean (4.edu_fair), predict(pr outcome(4))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._predict}:{space 1}{res:Marginal predicted mean (5.edu_fair), predict(pr outcome(5))}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#_at {c |}
{space 7}1  1  {c |}{col 14}{res}{space 2} .4525538{col 26}{space 2} .0288033{col 37}{space 1}   15.71{col 46}{space 3}0.000{col 54}{space 4} .3961004{col 67}{space 3} .5090072
{txt}{space 7}1  2  {c |}{col 14}{res}{space 2} .4346359{col 26}{space 2}  .027779{col 37}{space 1}   15.65{col 46}{space 3}0.000{col 54}{space 4} .3801902{col 67}{space 3} .4890817
{txt}{space 7}1  3  {c |}{col 14}{res}{space 2} .4168513{col 26}{space 2} .0269259{col 37}{space 1}   15.48{col 46}{space 3}0.000{col 54}{space 4} .3640776{col 67}{space 3} .4696251
{txt}{space 7}1  4  {c |}{col 14}{res}{space 2} .3992353{col 26}{space 2} .0262585{col 37}{space 1}   15.20{col 46}{space 3}0.000{col 54}{space 4} .3477696{col 67}{space 3}  .450701
{txt}{space 7}1  5  {c |}{col 14}{res}{space 2} .3818222{col 26}{space 2} .0257834{col 37}{space 1}   14.81{col 46}{space 3}0.000{col 54}{space 4} .3312875{col 67}{space 3} .4323568
{txt}{space 7}1  6  {c |}{col 14}{res}{space 2} .3646449{col 26}{space 2} .0254986{col 37}{space 1}   14.30{col 46}{space 3}0.000{col 54}{space 4} .3146686{col 67}{space 3} .4146212
{txt}{space 7}1  7  {c |}{col 14}{res}{space 2} .3477352{col 26}{space 2} .0253928{col 37}{space 1}   13.69{col 46}{space 3}0.000{col 54}{space 4} .2979663{col 67}{space 3} .3975042
{txt}{space 7}1  8  {c |}{col 14}{res}{space 2} .3311232{col 26}{space 2} .0254472{col 37}{space 1}   13.01{col 46}{space 3}0.000{col 54}{space 4} .2812477{col 67}{space 3} .3809987
{txt}{space 7}1  9  {c |}{col 14}{res}{space 2} .3148371{col 26}{space 2} .0256367{col 37}{space 1}   12.28{col 46}{space 3}0.000{col 54}{space 4}   .26459{col 67}{space 3} .3650841
{txt}{space 7}1 10  {c |}{col 14}{res}{space 2} .2989033{col 26}{space 2} .0259329{col 37}{space 1}   11.53{col 46}{space 3}0.000{col 54}{space 4} .2480758{col 67}{space 3} .3497308
{txt}{space 7}2  1  {c |}{col 14}{res}{space 2} .2936191{col 26}{space 2} .0082727{col 37}{space 1}   35.49{col 46}{space 3}0.000{col 54}{space 4} .2774049{col 67}{space 3} .3098333
{txt}{space 7}2  2  {c |}{col 14}{res}{space 2} .2967873{col 26}{space 2} .0077799{col 37}{space 1}   38.15{col 46}{space 3}0.000{col 54}{space 4} .2815389{col 67}{space 3} .3120357
{txt}{space 7}2  3  {c |}{col 14}{res}{space 2} .2994034{col 26}{space 2} .0074003{col 37}{space 1}   40.46{col 46}{space 3}0.000{col 54}{space 4}  .284899{col 67}{space 3} .3139079
{txt}{space 7}2  4  {c |}{col 14}{res}{space 2} .3014524{col 26}{space 2} .0071228{col 37}{space 1}   42.32{col 46}{space 3}0.000{col 54}{space 4}  .287492{col 67}{space 3} .3154129
{txt}{space 7}2  5  {c |}{col 14}{res}{space 2} .3029224{col 26}{space 2} .0069364{col 37}{space 1}   43.67{col 46}{space 3}0.000{col 54}{space 4} .2893272{col 67}{space 3} .3165176
{txt}{space 7}2  6  {c |}{col 14}{res}{space 2} .3038047{col 26}{space 2} .0068339{col 37}{space 1}   44.46{col 46}{space 3}0.000{col 54}{space 4} .2904105{col 67}{space 3} .3171989
{txt}{space 7}2  7  {c |}{col 14}{res}{space 2} .3040942{col 26}{space 2}  .006814{col 37}{space 1}   44.63{col 46}{space 3}0.000{col 54}{space 4}  .290739{col 67}{space 3} .3174495
{txt}{space 7}2  8  {c |}{col 14}{res}{space 2} .3037893{col 26}{space 2} .0068832{col 37}{space 1}   44.13{col 46}{space 3}0.000{col 54}{space 4} .2902985{col 67}{space 3} .3172801
{txt}{space 7}2  9  {c |}{col 14}{res}{space 2} .3028916{col 26}{space 2} .0070543{col 37}{space 1}   42.94{col 46}{space 3}0.000{col 54}{space 4} .2890655{col 67}{space 3} .3167177
{txt}{space 7}2 10  {c |}{col 14}{res}{space 2} .3014065{col 26}{space 2}  .007344{col 37}{space 1}   41.04{col 46}{space 3}0.000{col 54}{space 4} .2870125{col 67}{space 3} .3158005
{txt}{space 7}3  1  {c |}{col 14}{res}{space 2} .1359453{col 26}{space 2} .0093616{col 37}{space 1}   14.52{col 46}{space 3}0.000{col 54}{space 4} .1175968{col 67}{space 3} .1542937
{txt}{space 7}3  2  {c |}{col 14}{res}{space 2}   .14149{col 26}{space 2} .0090691{col 37}{space 1}   15.60{col 46}{space 3}0.000{col 54}{space 4} .1237149{col 67}{space 3}  .159265
{txt}{space 7}3  3  {c |}{col 14}{res}{space 2} .1469647{col 26}{space 2} .0088185{col 37}{space 1}   16.67{col 46}{space 3}0.000{col 54}{space 4} .1296808{col 67}{space 3} .1642487
{txt}{space 7}3  4  {c |}{col 14}{res}{space 2} .1523444{col 26}{space 2} .0086134{col 37}{space 1}   17.69{col 46}{space 3}0.000{col 54}{space 4} .1354624{col 67}{space 3} .1692264
{txt}{space 7}3  5  {c |}{col 14}{res}{space 2} .1576036{col 26}{space 2} .0084536{col 37}{space 1}   18.64{col 46}{space 3}0.000{col 54}{space 4} .1410348{col 67}{space 3} .1741724
{txt}{space 7}3  6  {c |}{col 14}{res}{space 2} .1627165{col 26}{space 2} .0083351{col 37}{space 1}   19.52{col 46}{space 3}0.000{col 54}{space 4}   .14638{col 67}{space 3} .1790529
{txt}{space 7}3  7  {c |}{col 14}{res}{space 2} .1676575{col 26}{space 2} .0082501{col 37}{space 1}   20.32{col 46}{space 3}0.000{col 54}{space 4} .1514875{col 67}{space 3} .1838275
{txt}{space 7}3  8  {c |}{col 14}{res}{space 2} .1724013{col 26}{space 2} .0081882{col 37}{space 1}   21.05{col 46}{space 3}0.000{col 54}{space 4} .1563527{col 67}{space 3}   .18845
{txt}{space 7}3  9  {c |}{col 14}{res}{space 2}  .176923{col 26}{space 2} .0081368{col 37}{space 1}   21.74{col 46}{space 3}0.000{col 54}{space 4} .1609752{col 67}{space 3} .1928709
{txt}{space 7}3 10  {c |}{col 14}{res}{space 2} .1811983{col 26}{space 2} .0080825{col 37}{space 1}   22.42{col 46}{space 3}0.000{col 54}{space 4} .1653568{col 67}{space 3} .1970397
{txt}{space 7}4  1  {c |}{col 14}{res}{space 2} .0839661{col 26}{space 2} .0093268{col 37}{space 1}    9.00{col 46}{space 3}0.000{col 54}{space 4}  .065686{col 67}{space 3} .1022463
{txt}{space 7}4  2  {c |}{col 14}{res}{space 2} .0896105{col 26}{space 2}  .009377{col 37}{space 1}    9.56{col 46}{space 3}0.000{col 54}{space 4} .0712318{col 67}{space 3} .1079891
{txt}{space 7}4  3  {c |}{col 14}{res}{space 2} .0954439{col 26}{space 2} .0094615{col 37}{space 1}   10.09{col 46}{space 3}0.000{col 54}{space 4} .0768997{col 67}{space 3} .1139881
{txt}{space 7}4  4  {c |}{col 14}{res}{space 2} .1014548{col 26}{space 2} .0095903{col 37}{space 1}   10.58{col 46}{space 3}0.000{col 54}{space 4} .0826581{col 67}{space 3} .1202515
{txt}{space 7}4  5  {c |}{col 14}{res}{space 2} .1076297{col 26}{space 2} .0097721{col 37}{space 1}   11.01{col 46}{space 3}0.000{col 54}{space 4} .0884768{col 67}{space 3} .1267826
{txt}{space 7}4  6  {c |}{col 14}{res}{space 2} .1139532{col 26}{space 2} .0100127{col 37}{space 1}   11.38{col 46}{space 3}0.000{col 54}{space 4} .0943287{col 67}{space 3} .1335777
{txt}{space 7}4  7  {c |}{col 14}{res}{space 2} .1204083{col 26}{space 2} .0103149{col 37}{space 1}   11.67{col 46}{space 3}0.000{col 54}{space 4} .1001914{col 67}{space 3} .1406251
{txt}{space 7}4  8  {c |}{col 14}{res}{space 2} .1269759{col 26}{space 2} .0106779{col 37}{space 1}   11.89{col 46}{space 3}0.000{col 54}{space 4} .1060476{col 67}{space 3} .1479041
{txt}{space 7}4  9  {c |}{col 14}{res}{space 2} .1336353{col 26}{space 2}  .011097{col 37}{space 1}   12.04{col 46}{space 3}0.000{col 54}{space 4} .1118857{col 67}{space 3}  .155385
{txt}{space 7}4 10  {c |}{col 14}{res}{space 2} .1403644{col 26}{space 2} .0115642{col 37}{space 1}   12.14{col 46}{space 3}0.000{col 54}{space 4} .1176989{col 67}{space 3} .1630298
{txt}{space 7}5  1  {c |}{col 14}{res}{space 2} .0339157{col 26}{space 2} .0061962{col 37}{space 1}    5.47{col 46}{space 3}0.000{col 54}{space 4} .0217713{col 67}{space 3}   .04606
{txt}{space 7}5  2  {c |}{col 14}{res}{space 2} .0374764{col 26}{space 2} .0065426{col 37}{space 1}    5.73{col 46}{space 3}0.000{col 54}{space 4}  .024653{col 67}{space 3} .0502997
{txt}{space 7}5  3  {c |}{col 14}{res}{space 2} .0413366{col 26}{space 2} .0069312{col 37}{space 1}    5.96{col 46}{space 3}0.000{col 54}{space 4} .0277517{col 67}{space 3} .0549215
{txt}{space 7}5  4  {c |}{col 14}{res}{space 2}  .045513{col 26}{space 2}  .007373{col 37}{space 1}    6.17{col 46}{space 3}0.000{col 54}{space 4} .0310623{col 67}{space 3} .0599638
{txt}{space 7}5  5  {c |}{col 14}{res}{space 2} .0500222{col 26}{space 2}   .00788{col 37}{space 1}    6.35{col 46}{space 3}0.000{col 54}{space 4} .0345777{col 67}{space 3} .0654667
{txt}{space 7}5  6  {c |}{col 14}{res}{space 2} .0548807{col 26}{space 2}  .008465{col 37}{space 1}    6.48{col 46}{space 3}0.000{col 54}{space 4} .0382897{col 67}{space 3} .0714717
{txt}{space 7}5  7  {c |}{col 14}{res}{space 2} .0601047{col 26}{space 2} .0091407{col 37}{space 1}    6.58{col 46}{space 3}0.000{col 54}{space 4} .0421894{col 67}{space 3} .0780201
{txt}{space 7}5  8  {c |}{col 14}{res}{space 2} .0657103{col 26}{space 2} .0099194{col 37}{space 1}    6.62{col 46}{space 3}0.000{col 54}{space 4} .0462686{col 67}{space 3} .0851521
{txt}{space 7}5  9  {c |}{col 14}{res}{space 2}  .071713{col 26}{space 2} .0108127{col 37}{space 1}    6.63{col 46}{space 3}0.000{col 54}{space 4} .0505205{col 67}{space 3} .0929054
{txt}{space 7}5 10  {c |}{col 14}{res}{space 2} .0781275{col 26}{space 2} .0118305{col 37}{space 1}    6.60{col 46}{space 3}0.000{col 54}{space 4} .0549401{col 67}{space 3}  .101315
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. 
. 
. 
. marginsplot,recastci(rarea)

{text}{p 2 6 2}Variables that uniquely identify margins: class _outcome{p_end}
{res}
{com}. 
. 
. 
. 
. 
. meoprobit edu_fair class income employer high_skilled education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,153
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     298.6
{col 63}{txt}max{col 67}={res}{col 69}     1,063

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   277.26
{txt}Log likelihood = {res}-14879.125{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    edu_fair{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0563778{col 26}{space 2} .0075537{col 37}{space 1}    7.46{col 46}{space 3}0.000{col 54}{space 4} .0415729{col 67}{space 3} .0711827
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0453686{col 26}{space 2} .0108467{col 37}{space 1}    4.18{col 46}{space 3}0.000{col 54}{space 4} .0241095{col 67}{space 3} .0666276
{txt}{space 4}employer {c |}{col 14}{res}{space 2}  .081399{col 26}{space 2} .0434819{col 37}{space 1}    1.87{col 46}{space 3}0.061{col 54}{space 4} -.003824{col 67}{space 3} .1666221
{txt}high_skilled {c |}{col 14}{res}{space 2} .0111899{col 26}{space 2} .0255209{col 37}{space 1}    0.44{col 46}{space 3}0.661{col 54}{space 4}-.0388301{col 67}{space 3} .0612099
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0356169{col 26}{space 2} .0097477{col 37}{space 1}    3.65{col 46}{space 3}0.000{col 54}{space 4} .0165117{col 67}{space 3}  .054722
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0098002{col 26}{space 2} .0072448{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-.0239998{col 67}{space 3} .0043994
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.1126795{col 26}{space 2} .0223124{col 37}{space 1}   -5.05{col 46}{space 3}0.000{col 54}{space 4} -.156411{col 67}{space 3} -.068948
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0227148{col 26}{space 2} .0230133{col 37}{space 1}   -0.99{col 46}{space 3}0.324{col 54}{space 4}-.0678199{col 67}{space 3} .0223904
{txt}{space 4}religion {c |}{col 14}{res}{space 2} -.005062{col 26}{space 2}  .027586{col 37}{space 1}   -0.18{col 46}{space 3}0.854{col 54}{space 4}-.0591295{col 67}{space 3} .0490055
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0449806{col 26}{space 2} .0093695{col 37}{space 1}    4.80{col 46}{space 3}0.000{col 54}{space 4} .0266167{col 67}{space 3} .0633445
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.0852006{col 26}{space 2} .0301569{col 37}{space 1}   -2.83{col 46}{space 3}0.005{col 54}{space 4} -.144307{col 67}{space 3}-.0260943
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2} -.154554{col 26}{space 2} .1040248{col 37}{space 1}   -1.49{col 46}{space 3}0.137{col 54}{space 4} -.358439{col 67}{space 3} .0493309
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} .6282093{col 26}{space 2} .1042421{col 37}{space 1}    6.03{col 46}{space 3}0.000{col 54}{space 4} .4238984{col 67}{space 3} .8325201
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} 1.316641{col 26}{space 2} .1045709{col 37}{space 1}   12.59{col 46}{space 3}0.000{col 54}{space 4} 1.111686{col 67}{space 3} 1.521596
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2}  2.15189{col 26}{space 2} .1054817{col 37}{space 1}   20.40{col 46}{space 3}0.000{col 54}{space 4}  1.94515{col 67}{space 3}  2.35863
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .2160075{col 26}{space 2} .0547783{col 54}{space 4} .1314041{col 67}{space 3} .3550821
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1146.39{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. margins, at(class =(1(1)10)) atmeans noatlegend 
{res}
{txt}Adjusted predictions{col 49}Number of obs{col 67}= {res}    10,153
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Marginal predicted mean (1.edu_fair), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Marginal predicted mean (2.edu_fair), predict(pr outcome(2))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Marginal predicted mean (3.edu_fair), predict(pr outcome(3))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._predict}:{space 1}{res:Marginal predicted mean (4.edu_fair), predict(pr outcome(4))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._predict}:{space 1}{res:Marginal predicted mean (5.edu_fair), predict(pr outcome(5))}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
_predict#_at {c |}
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{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{com}. 
. 
. 
. marginsplot,recastci(rarea)

{text}{p 2 6 2}Variables that uniquely identify margins: class _outcome{p_end}
{res}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\research\works\주제14_내로남불_교육\politics and policy\data\code.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}20 Mar 2025, 18:05:29
{txt}{.-}
{smcl}
{txt}{sf}{ul off}